Multi-feature 360 Video Quality Estimation
This addresses the need for accurate quality estimation in 360-degree video systems, which is crucial for improving user experience in applications like virtual reality, but it is incremental as it builds on existing quality metrics by combining them adaptively.
The authors tackled the problem of visual quality assessment for 360-degree videos by proposing a method that computes multiple spatio-temporal features on viewports and learns a model to combine them, resulting in a metric that outperforms state-of-the-art methods in experiments on the largest available dataset and cross-dataset validation.
We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quality features on viewports extracted from 360-degree videos. A new model is learnt to properly combine these features into a metric that closely matches subjective quality scores. The main motivations for the proposed approach are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) the use of viewports easily supports different projection methods being used in current 360-degree video systems; and 3) no individual objective image quality metric always performs the best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions. Experimental results, based on both the largest available 360-degree videos quality dataset and a cross-dataset validation, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.